Genome Scans

div <- read.csv(file="/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/GenomeScanInput.inclInvariant.csv")

Exploring genetic variation

First, we look at the distribution of variation across the genome.

Manhattan plots

manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/manhattans_for_pub/Fst.UKUS.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()

## quartz_off_screen 
##                 2

manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/manhattans_for_pub/Fst.AUUK.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()

## quartz_off_screen 
##                 2

manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_USAU", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.USAU.Manhattan.pdf",w=12,h=3)
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_USAU", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))
dev.off()

## quartz_off_screen 
##                 2

Identifying outliers

quantile(div$WEIGHTED_FST_AUUK, c(.9,.95,.99,.999)) 

##        90%        95%        99%      99.9% 
## 0.05954616 0.07488818 0.12056008 0.20794197

quantile(div$WEIGHTED_FST_UKUS, c(.9,.95,.99,.999)) 

##        90%        95%        99%      99.9% 
## 0.03921836 0.05206951 0.09023700 0.17768016

mean(div$WEIGHTED_FST_AUUK) + 5*sd(div$WEIGHTED_FST_AUUK)

## [1] 0.1588041

mean(div$WEIGHTED_FST_UKUS) + 5*sd(div$WEIGHTED_FST_UKUS)

## [1] 0.1170443

div.outliers.AUUK <- div[which(div$WEIGHTED_FST_AUUK > quantile(div$WEIGHTED_FST_AUUK,.99)),]
div.outliers.USUK <- div[which(div$WEIGHTED_FST_UKUS > quantile(div$WEIGHTED_FST_UKUS,.99)),]

div.hifst.AUUK <- div[which(div$WEIGHTED_FST_AUUK > 0.1),]
div.hifst.UKUS <- div[which(div$WEIGHTED_FST_UKUS > 0.1),]

unique(div.outliers.USUK$CHROM)

##  [1] 11.00 12.00 13.00 17.00  1.25  1.00 28.00  2.00  3.00  4.50  4.00  6.00
## [13]  0.00

length(div.outliers.USUK$SNP)

## [1] 201

write.csv(div.outliers.USUK,"/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/FstOutliers.USUK.csv")

unique(div.outliers.AUUK$CHROM)

##  [1] 10.00 12.00 13.00 17.00 18.00  1.25  1.00 23.00 27.00  2.00  3.00  4.50
## [13]  4.00  5.00  6.00  7.00  8.00  0.00

length(div.outliers.AUUK$SNP)

## [1] 201

write.csv(div.outliers.AUUK,"/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/FstOutliers.AUUK.csv")

div.outliers.AUUK.lowFstUSAU <- div.outliers.AUUK[which(div.outliers.AUUK$WEIGHTED_FST_USAU < 0.01 ),] 
unique(div.outliers.AUUK.lowFstUSAU$CHROM)

## [1] 10.00 12.00 17.00  1.25  3.00  4.50  5.00  6.00  0.00

length(div.outliers.AUUK.lowFstUSAU$SNP)

## [1] 21

div.outliers.USUK.lowFstUSAU <- div.outliers.USUK[which(div.outliers.USUK$WEIGHTED_FST_USAU < 0.01 ),] 
unique(div.outliers.USUK.lowFstUSAU$CHROM)

## [1] 12.00 13.00  1.25  1.00  4.50  4.00  6.00  0.00

length(div.outliers.USUK.lowFstUSAU$SNP)

## [1] 34

intersect(unique(div.outliers.AUUK.lowFstUSAU$CHROM),unique(div.outliers.USUK.lowFstUSAU$CHROM))

## [1] 12.00  1.25  4.50  6.00  0.00

Possible parallel “selection” ?

Novel pi

What’s the impact of the genetic bottlenecks and subsequent expansion on genetic diversity in each invasion? What’s the difference in diversity between native and invasive ranges?

When comparing ancestral diversity to a bottlenecked invasive population, we’d expect to see lower diversity in the invasive range overall. This difference would be more pronounced in regions that had differentiated from the native range (e.g., where drift and/or selection are more pronounced).

Here, difference in pi > 0 means that diversity is higher in the native range than in the invasive.

library(fitdistrplus)

## Loading required package: MASS

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

## Loading required package: survival

summary(div$piUK.piAU)

##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0016886  0.0001104  0.0003213  0.0003534  0.0005514  0.0028157

qqnorm(div$piUK.piAU, pch = 16)
qqline(div$piUK.piAU, pch = 16)

descdist(div$piUK.piAU)

## summary statistics
## ------
## min:  -0.00168861   max:  0.00281566 
## median:  0.0003213 
## mean:  0.0003534214 
## estimated sd:  0.000351044 
## estimated skewness:  0.643205 
## estimated kurtosis:  4.80719

summary(div$piUK.piUS)

##       Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
## -0.0015761  0.0000535  0.0002299  0.0002464  0.0004189  0.0020663

qqnorm(div$piUK.piUS, pch = 16)
qqline(div$piUK.piUS, pch = 16)

descdist(div$piUK.piUS)

## summary statistics
## ------
## min:  -0.0015761   max:  0.00206628 
## median:  0.000229905 
## mean:  0.0002464126 
## estimated sd:  0.0002948161 
## estimated skewness:  0.2837006 
## estimated kurtosis:  4.635778

div.hiUKpi.vsAU <- div[which(div$piUK.piAU > 0),]
div.hiUKpi.vsUS <- div[which(div$piUK.piUS > 0),]
div.hiUKpi.both <- div.hiUKpi.vsAU[which(div$piUK.piUS > 0),]
length(div.hiUKpi.vsAU$piUK.piAU)/length(div$piUK.piAU) # % of windows that have higher pi in native

## [1] 0.8739165

length(div.hiUKpi.vsUS$piUK.piUS)/length(div$piUK.piUS) 

## [1] 0.8252964

length(div.hiUKpi.both$piUK.piUS)/length(div$piUK.piUS) # % of windows with higher pi in native for both invasive ranges

## [1] 0.8252964

If drift acting on novel mutations explains most of the differentiation, then where FST is high, diversity should also be higher in the invasions. Could also be due to weaker purifying selection during population expansion.

descdist(div.hifst.AUUK$piUK.piAU)

## summary statistics
## ------
## min:  -0.00113444   max:  0.0024697 
## median:  0.000142827 
## mean:  0.0003322577 
## estimated sd:  0.0006049086 
## estimated skewness:  0.9733542 
## estimated kurtosis:  3.891919

div.hifst.AUUK.hiUKpi <- div.hifst.AUUK[which(div.hifst.AUUK$piUK.piAU > 0),]
length(div.hifst.AUUK.hiUKpi$piUK.piAU)/length(div.hifst.AUUK$piUK.piAU) # % of high FST windows that have higher pi in native

## [1] 0.7035176

descdist(div.hifst.UKUS$piUK.piUS)

## summary statistics
## ------
## min:  -0.00125823   max:  0.00206628 
## median:  4.991515e-05 
## mean:  0.0001182706 
## estimated sd:  0.000505732 
## estimated skewness:  0.4618472 
## estimated kurtosis:  4.721092

div.hifst.UKUS.hiUKpi <- div.hifst.UKUS[which(div.hifst.UKUS$piUK.piUS > 0),]
length(div.hifst.UKUS.hiUKpi$piUK.piUS)/length(div.hifst.UKUS$piUK.piUS) 

## [1] 0.6776316

div.hiUKpi.both <- div.hifst.UKUS.hiUKpi[which(div.hifst.AUUK.hiUKpi$piUK.piUS > 0),]
length(div.hiUKpi.both$piUK.piUS)

## [1] 206

div.hifst.AUUK.hiAUpi <- div.hifst.AUUK[which(div.hifst.AUUK$piUK.piAU < 0),]
div.hifst.UKUS.hiUSpi <- div.hifst.UKUS[which(div.hifst.UKUS$piUK.piUS < 0),]

In a given window, if diversity in the invasive range is higher than that of the native range, it is possible that those variants are novel mutations. This filtering will tell us whether we should look at particular genotypes in these regions.

div.novelpi <- div %>% 
  filter((div$piUK.piAU < 0) & (div$piUK.piUS < 0))
# % of low native diversity SNPs are higher in diversity in both invasions
# sanity check based on calc above
length(div.novelpi$SNP)/length(div$SNP)

## [1] 0.06326592

div.novelUSpi <- div %>%
    filter(div$piUK.piUS < 0)
length(div.novelUSpi$SNP)/length(div$SNP)

## [1] 0.1746538

div.novelAUpi <- div %>%
    filter(div$piUK.piAU < 0)
length(div.novelAUpi$SNP)/length(div$SNP)

## [1] 0.1260835

How to test whether novel pi is evenly distributed across the genome? If even, then we expect to see a random sampling of chromosomes represented in this smaller dataset.

unique(div.novelpi$CHROM) # which chromosomes have higher invasive pi in both invasions

##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 26.00 27.00 28.00  2.00  3.00  4.50  4.00
## [25]  5.00  6.00  7.00  8.00  9.00 29.00  0.00

unique(div.novelUSpi$CHROM) # just in US

##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00 29.00  0.00

unique(div.novelAUpi$CHROM) # in AU

##  [1] 10.00 11.00 12.00 13.00 14.00 15.00 17.00 18.00 19.00  1.25  1.75  1.00
## [13] 20.00 21.00 22.00 23.00 24.00 25.00 26.00 27.00 28.00  2.00  3.00  4.50
## [25]  4.00  5.00  6.00  7.00  8.00  9.00 29.00  0.00

length(unique(div.novelpi$CHROM))/length(unique(div$CHROM))

## [1] 0.9393939

length(unique(div.novelUSpi$SNP)) # are most of these higher invasive pi regions also the moderately differentiated windows

## [1] 3506

length(unique(div.hifst.UKUS$SNP))

## [1] 152

length(unique(div.hifst.UKUS$SNP))/length(unique(div.novelUSpi$SNP))

## [1] 0.04335425

length(unique(div.novelAUpi$SNP))

## [1] 2531

length(unique(div.hifst.AUUK$SNP))

## [1] 398

length(unique(div.hifst.AUUK$SNP))/length(unique(div.novelAUpi$SNP))

## [1] 0.1572501

This is a different calculation than asking whether differentiated windows are also high in invasive diversity, since we’re drawing from different sets.

Can we color points in the manhattan plot by diff in diversity

SNP.novelUSpi <- div.novelUSpi$SNP
SNP.novelAUpi <- div.novelAUpi$SNP
SNP.novelAUpi.hifstonly <- div.hifst.AUUK.hiAUpi$SNP
SNP.novelUSpi.hifstonly <- div.hifst.UKUS.hiUSpi$SNP
length(div.hifst.UKUS.hiUSpi$SNP) 

## [1] 49

length(div.hifst.AUUK.hiAUpi$SNP)

## [1] 118

manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelUSpi.hifstonly,
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

#pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.UKUS.Manhattan.pdf",w=12,h=3)
#dev.off()
manhattan(div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelAUpi.hifstonly,
          cex=1,cex.axis=1, chrlabs=c("Z",1,"1A","1B",2,3,4,"4A",5:29))

#pdf("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Fst.AUUK.Manhattan.pdf",w=12,h=3)
#dev.off()

What’s going on at outlier windows in particular?

div.outliers.hiAUpi <- div.outliers.AUUK[which(div.outliers.AUUK$piUK.piAU < 0),]
length(div.outliers.hiAUpi$SNP)

## [1] 73

length(div.outliers.hiAUpi$SNP)/length(div.outliers.AUUK$SNP)

## [1] 0.3631841

% of FST outlier windows have higher diversity in AU

div.outliers.hiUSpi <- div.outliers.USUK[which(div.outliers.USUK$piUK.piUS < 0),]
length(div.outliers.hiUSpi$SNP) 

## [1] 57

length(div.outliers.hiUSpi$SNP)/length(div.outliers.USUK$SNP)

## [1] 0.2835821

% of FST outlier windows have higher diversity in US

intersect(div.outliers.hiUSpi$CHROM,div.outliers.hiAUpi$CHROM)

## [1] 12.00  1.25  1.00  2.00  3.00  6.00  0.00

unique(div.outliers.hiAUpi$CHROM)

## [1] 12.00  1.25  1.00 27.00  2.00  3.00  5.00  6.00  0.00

unique(div.outliers.hiUSpi$CHROM) 

## [1] 12.00 17.00  1.25  1.00  2.00  3.00  4.00  6.00  0.00

Figures: Diversity underlying candidate peaks

The plots below are based on code from Gemma Clucas.

Chromosome 2

chrom2.div <- div[which(div$CHROM==2),]
length(chrom2.div$SNP) # how many windows total (need for calculating distance to centromere)

## [1] 2997

manhattan(chrom2.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelAUpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom2.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

manhattan(chrom2.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelUSpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom2.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

chrom2.div.small <- chrom2.div[which(chrom2.div$SNP < 8980),]
chrom2.div.small <- chrom2.div.small[which(chrom2.div.small$SNP > 8600),]
# 39200001 to 51650000

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1)) # set rows
par(mar=c(0,2,0.5,2)) # set margins for each plot
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.5))
lines((chrom2.div.small$BIN_START), chrom2.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.5))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_US, col="#2c81a8", lwd=2)
axis(side=4, ylim=c(0,0.04))
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom2.div.small$BIN_START), chrom2.div.small$PI_UK, col="#39C855", lwd=1)
abline(v=76290000, col="black", lwd=0.5) # centromere position
par(mar=c(1,2,1,2))
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.2))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.2))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_UK, type="n", xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.2))
lines((chrom2.div.small$BIN_START), chrom2.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.2)) # tajima's D axis
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/Chromosome2.ManhattanZoom.pdf", type="pdf")

## quartz_off_screen 
##                 2

Chromosome 6

chrom6.div <- div[which(div$CHROM==6),]
# runs from 16155 to 16871
# chrom 6 = 716 50kb windows ("SNPs" here)

manhattan(chrom6.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelAUpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom6.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

manhattan(chrom6.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelUSpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom6.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

chrom6.div.small <- chrom6.div[which(chrom6.div$SNP < 16450),]
chrom6.div.small <- chrom6.div.small[which(chrom6.div.small$SNP > 16155),]

head(chrom6.div.small)

##           X CHROM BIN_START BIN_END N_VARIANTS WEIGHTED_FST_UKUS MEAN_FST_UKUS
## 16156 16156     6         1   50000       1598        0.00628527    0.00248457
## 16157 16157     6     50001  100000       1520        0.01449240    0.00843001
## 16158 16158     6    100001  150000       1528        0.02074420    0.01103080
## 16159 16159     6    150001  200000       1877        0.02639580    0.01648160
## 16160 16160     6    200001  250000       1672        0.03637210    0.02209860
## 16161 16161     6    250001  300000       1544        0.02546480    0.01708850
##       WEIGHTED_FST_AUUK MEAN_FST_AUUK WEIGHTED_FST_USAU MEAN_FST_USAU
## 16156        0.00117863   0.000823727         0.0239944     0.0166647
## 16157        0.01051320   0.006750700         0.0183299     0.0131435
## 16158        0.01054440   0.006650770         0.0303602     0.0190195
## 16159        0.02640960   0.016924400         0.0512444     0.0351528
## 16160        0.03746390   0.022292300         0.0618308     0.0380619
## 16161        0.01679430   0.010967400         0.0430600     0.0295965
##            PI_UK      PI_US      PI_AU TajimaD_UK TajimaD_US TajimaD_AU   SNP
## 16156 0.00824049 0.00734212 0.00781269 -0.2257440 -0.0544624  0.0818164 16156
## 16157 0.00724211 0.00692353 0.00692622 -0.2808070 -0.0474255 -0.1486820 16157
## 16158 0.00740661 0.00672015 0.00713702 -0.3101470 -0.1485980  0.0147164 16158
## 16159 0.00969194 0.00855432 0.00872315 -0.1835880  0.1673420  0.3035250 16159
## 16160 0.00856017 0.00793468 0.00791952 -0.0547287  0.1293050  0.0472655 16160
## 16161 0.00798826 0.00745739 0.00742256 -0.1198140  0.2854490  0.0209135 16161
##        piUK.piAU  piUK.piUS
## 16156 0.00042780 0.00089837
## 16157 0.00031589 0.00031858
## 16158 0.00026959 0.00068646
## 16159 0.00096879 0.00113762
## 16160 0.00064065 0.00062549
## 16161 0.00056570 0.00053087

chrom6.div.hifst <- chrom6.div[which(chrom6.div$SNP < 16281),]
chrom6.div.hifst <- chrom6.div.small[which(chrom6.div.small$SNP > 16263),]

# AUUK high fst: window 5350001 to 6300001 on Chrom 6
# "SNP" 16263 to 16281

mean(chrom6.div.hifst$PI_UK) 

## [1] 0.005018394

mean(chrom6.div.hifst$PI_US) 

## [1] 0.004798872

mean(chrom6.div.hifst$PI_AU) 

## [1] 0.00465142

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_US, col="#2c81a8", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom6.div.small$BIN_START), chrom6.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_UK, type="n", xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,2.4))
lines((chrom6.div.small$BIN_START), chrom6.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,2.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/manhattans_for_pub/Chromosome6.ManhattanZoom.pdf", type="pdf")

## quartz_off_screen 
##                 2

Chromosome 1

chrom1.div <- div[which(div$CHROM==1),]
length(chrom1.div$SNP)

## [1] 2279

manhattan(chrom1.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelAUpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom1.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

manhattan(chrom1.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelUSpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom1.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

chrom1.div.small <- chrom1.div[which(chrom1.div$SNP < 6800),]
chrom1.div.small <- chrom1.div.small[which(chrom1.div.small$SNP > 6350),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.04))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.04))
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.04))
lines((chrom1.div.small$BIN_START), chrom1.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1.div.small$BIN_START), chrom1.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/manhattans_for_pub/Chromosome1.ManhattanZoom.pdf", type="pdf")

## quartz_off_screen 
##                 2

Chromosome 1A

chrom1A.div <- div[which(div$CHROM==1.25),]
# 2896 to 4342
# 1446 windows, 3 ticks


manhattan(chrom1A.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelAUpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom1A.div, chr = "CHROM", bp = "BIN_START", snp =
## "SNP", : You're trying to highlight SNPs that don't exist in your results.

manhattan(chrom1A.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelUSpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom1A.div, chr = "CHROM", bp = "BIN_START", snp =
## "SNP", : You're trying to highlight SNPs that don't exist in your results.

chrom1A.div.small <- chrom1A.div[which(chrom1A.div$SNP < 4000),]
chrom1A.div.small <- chrom1A.div.small[which(chrom1A.div.small$SNP > 3700),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom1A.div.small$BIN_START), chrom1A.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/manhattans_for_pub/Chromosome1A.ManhattanZoom.pdf", type="pdf")

## quartz_off_screen 
##                 2

Chromosome 4

chrom4.div <- div[which(div$CHROM==4),]
# 13506 to 14923,
# 1417 windows, 7 ticks - peak ~500 windows from start 


manhattan(chrom4.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelAUpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom4.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

manhattan(chrom4.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelUSpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom4.div, chr = "CHROM", bp = "BIN_START", snp = "SNP", :
## You're trying to highlight SNPs that don't exist in your results.

chrom4.div.small <- chrom4.div[which(chrom4.div$SNP < 14150),]
chrom4.div.small <- chrom4.div.small[which(chrom4.div.small$SNP > 13850),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4.div.small$BIN_START), chrom4.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4.div.small$BIN_START), chrom4.div.small$TajimaD_UK, col="#39C855", lwd=1)

axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/manhattans_for_pub/Chromosome4.ManhattanZoom.pdf", type="pdf")

## quartz_off_screen 
##                 2

Chromosome 4A

chrom4A.div <- div[which(div$CHROM==4.5),]
# 13097 to 13505
# 408 windows, 4 ticks

manhattan(chrom4A.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_AUUK", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelAUpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom4A.div, chr = "CHROM", bp = "BIN_START", snp =
## "SNP", : You're trying to highlight SNPs that don't exist in your results.

manhattan(chrom4A.div, chr="CHROM", bp="BIN_START", snp="SNP", p="WEIGHTED_FST_UKUS", 
          ylim=c(0,0.41),ylab=NA,xlab=NA,logp = FALSE,col=c("grey45","grey65"),
          highlight=SNP.novelUSpi.hifstonly,
          cex=1,cex.axis=1)

## Warning in manhattan(chrom4A.div, chr = "CHROM", bp = "BIN_START", snp =
## "SNP", : You're trying to highlight SNPs that don't exist in your results.

chrom4A.div.small <- chrom4A.div[which(chrom4A.div$SNP < 13400),]
chrom4A.div.small <- chrom4A.div.small[which(chrom4A.div.small$SNP > 13050),]

quartz(height=5,width=7)
options(scipen=999)
par(mfrow=c(2,1))
par(mar=c(0,2,0.5,2))
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_AUUK, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_AUUK, col="#F2C14E",lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_UKUS, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_UKUS, col="#2c81a8", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_USAU, type="n", bty="n", axes=FALSE,  ylim=c(-0.2,0.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$WEIGHTED_FST_USAU, col="grey50", lwd=1)
axis(side=2,ylim=c(-0.2,0.4))
abline(h=quantile(div$WEIGHTED_FST_UKUS,.99), col="#2c81a8", lwd=0.5)
abline(h=quantile(div$WEIGHTED_FST_AUUK,.99), col="#F2C14E", lwd=0.5)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_AU, type="n", axes=FALSE, bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_AU, col="#F8DD9E", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_US, col="#66A3C0", lwd=2)
axis(side=4, ylim=c(0,0.03))
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(0,0.03))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$PI_UK, col="#39C855", lwd=1)
par(mar=c(1,2,1,2))
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_AU, type="n",axes=FALSE, bty="n", xlab=NA, ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_AU, col="#F2C14E", lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_US, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_US, col="#2c81a8",lwd=2)
par(new=T)
plot((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_UK, type="n", axes=FALSE, xlab=NA, ylab=NA,  bty="n", ylim=c(-2.4,3.4))
lines((chrom4A.div.small$BIN_START), chrom4A.div.small$TajimaD_UK, col="#39C855", lwd=1)
axis(side=2, ylim=c(-2.4,3.4))
axis(side=1)

quartz.save("/Users/nataliehofmeister/Documents/Ch3-Global-RESEQ/analysis/R/manhattans_for_pub/Chromosome4A.ManhattanZoom.pdf", type="pdf")

## quartz_off_screen 
##                 2